Correlation between the Western Ontario and 
McMaster Universities Osteoarthritis Index (WOMAC) 
Scores and the Stability Metrics in Patients with Knee 
Osteoarthritis

Hosseini, Iman; Ashtiani, Mohammed N.; Bahrpeyma, Farid

doi:10.52547/ijmpp.7.3.741

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…Increased COP variability is an important predictive indicators for falls in older adults [ 40 ]. Previous study had reported that the A/P variation of COP was greater in patients with moderate KOA than that in healthy individuals [ 41 ]. An important reason of the increased A/P variation of COP in ROA group may due to the impaired knee proprioception [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

“…Previous study had reported that the A/P variation of COP was greater in patients with moderate KOA than that in healthy individuals [ 41 ]. An important reason of the increased A/P variation of COP in ROA group may due to the impaired knee proprioception [ 41 ]. Impaired knee proprioception will affect the motion and position sense of the lower limbs during walking, consequently reducing the balance control ability and gait stability [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

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Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

Li,

Xie

et al. 2024

J NeuroEngineering Rehabil

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Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

Li,

Xie

et al. 2024

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

Correlation between the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Scores and the Stability Metrics in Patients with Knee Osteoarthritis

Abstract: How to cite this article Hosseini I., Najafi Ashtiani M. Bahrpeyma F. Correlation between the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Scores and the Stability Metrics in Patients with Knee Osteoarthritis. IJMPP. 2022; 7(3): 741-749.

Cited by 1 publication

References 31 publications

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

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